[Submitted in IEEE J-BHI]
Retinopathy refers to any damage in the retina that causes visual impairments or even blindness. Identification of retinal lesions plays a vital role in accurately grading retinopathy and for its effective treatment. Optical coherence tomography (OCT) imaging is the most popular non-invasive technique used for the retinal examination due to its ability to screen abnormalities in early stages. Many researchers have presented studies on OCT based retinal image analysis over the past. However, to our best knowledge, there is no framework yet available which can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework which extracts retinal lesions such as intra-retinal fluid, sub-retinal fluid, hard exudates, drusen and chorioretinal abnormalities (including fibrotic scars and choroidal neovascular membranes) from multi-vendor OCT scans. Furthermore, it utilizes them for the lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been trained using 113,261 retinal OCT scans from which 112,261 scans were used for training and 1,000 scans were used for the validation purposes. Furthermore, it has been rigorously tested on 43,613 scans from five highly complex publicly available datasets where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the F₁ score of 99.52% for correctly classifying the retinopathy cases. The source code of RAG-FW is available at http://biomisa.org/index.php/downloads/.